Bibliographic record
Abstract
In this paper, I invoke the post-World-War-II Italian public intellectual Pier Paolo Pasolini, juxtaposing Pasolini’s public pedagogy – his subjective presence always attuned to the historical moment - with a 2013 essay composed by contemporary U.S. scholars Jake Burdick and Jennifer Sandlin, who perform what I term discursive engineering, dismissing canonical concepts of education (without argument or evidence), apparently fantasizing that by changing what we say we can change the world. Alas, Pasolini knew better. No securely tenured professor, Pasolini risked his life to teach the Italian public, calling out the catastrophic path humanity has taken, specifically substituting virtuality for actuality, technologization that we imagine leaves us immune to the consequences of unbridled capitalism. Focused on Pasolini’s unfinished novel Petrolio (petroleum or crude oil) and a 2014 film focused on the final few days before Pasolini was assassinated, I conclude this curricular juxtaposition hoping to carve out what Tetsuo Aoki termed a generative space of difference, wherein we might re-experience – even reactivate – an earlier anthropological moment when we were still – sort of – “human.”
Fetched live from OpenAlex and de-inverted. Abstracts are not stored in this database: the inverted indexes are 8.6 GB of the frame’s 9.3 GB of text, and the host has 13 GB free.
How this classification was reachedexpand
Full frame machine prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. The Gemma side is a direct model label for every work in the frame, read from the title-only record. The Codex side is a classifier learned from the 10,348 direct Codex labels and calibrated to design-weighted sample rates; fields without enough sample support carry no Codex call. Candidate is the union of the two sides; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels.
Distilled classifier scores by category (both heads)
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.002 | 0.004 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.005 | 0.030 |
| Scholarly communication | 0.008 | 0.007 |
| Open science | 0.001 | 0.005 |
| Research integrity | 0.002 | 0.004 |
| Insufficient payload (model declined to judge) | 0.005 | 0.001 |
Machine scores (provisional)
The two teacher heads of the student model, read on this work. A score orders the frame for review; it never asserts a category, and the validation status ships verbatim with every row.
Baseline scores from an immature model (maturity gate not passed, 7 training rounds). Scores rank; they never assert a category.
score_only:v0-immature-baseline · verbatim from the scoring run: score_only means the number may rank works, and no category label ships from itClassification
machine, unvalidatedMachine predicted; a candidate call from one source (direct Gemma or distilled Codex), not a consensus.
How this classification was reached, model by model and score by score, is at the end of the page under "How this classification was reached".